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作者：Tchetgen Tchetgen, Eric J.; Fulcher, Isabel R.; Shpitser, Ilya
作者单位：University of Pennsylvania; Harvard University; Harvard Medical School; Johns Hopkins University
摘要：Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The ...
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作者：Das, Srinjoy; Politis, Dimitris N.
作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：The model-free prediction principle of Politis has been successfully applied to general regression problems, as well as problems involving stationary time series. However, with long time series, for example, annual temperature measurements spanning over 100 years or daily financial returns spanning several years, it may be unrealistic to assume stationarity throughout the span of the dataset. In this article, we show how model-free prediction can be applied to handle time series that are only ...
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作者：Abadie, Alberto; Cattaneo, Matias D.
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作者：Ray, Kolyan; Szabo, Botond
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作者：Ma, Cong; Lu, Junwei; Liu, Han
作者单位：Princeton University; Harvard University; Harvard T.H. Chan School of Public Health; Northwestern University; Northwestern University
摘要：Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between brain regions under natural stimuli. We propose a modeling framework for ISA that is based on Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of a...
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作者：Ha, Min Jin; Stingo, Francesco Claudio; Baladandayuthapani, Veerabhadran
作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Florence; University of Michigan System; University of Michigan
摘要：Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model t...
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作者：Kuenzer, Thomas; Hormann, Siegfried; Kokoszka, Piotr
作者单位：Graz University of Technology; Colorado State University System; Colorado State University Fort Collins
摘要：We develop an expansion, similar in some respects to the Karhunen-Loeve expansion, but which is more suitable for functional data indexed by spatial locations on a grid. Unlike the traditional Karhunen-Loeve expansion, it takes into account the spatial dependence between the functions. By doing so, it provides a more efficient dimension reduction tool, both theoretically and in finite samples, for functional data with moderate spatial dependence. For such data, it also possesses other theoreti...
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作者：Chen, Haoyu; Lu, Wenbin; Song, Rui
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作者：Forastiere, Laura; Airoldi, Edoardo M.; Mealli, Fabrizia
作者单位：Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Florence
摘要：Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other units, such as their neighbors in the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended-say, to include the treatment of neighbors, and...
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作者：Scealy, Janice L.; Wood, Andrew T. A.
作者单位：Australian National University
摘要：Robust estimation of location for data on the unit sphere is an important problem in directional statistics even though the influence functions of the sample mean direction and other location estimators are bounded. A significant limitation of previous literature on this topic is that robust estimators and procedures have been developed under the assumption that the underlying population is rotationally symmetric. This assumption often does not hold with real data and in these cases there is a...
